| { | |
| "paper_id": "O08-5001", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:02:43.168648Z" | |
| }, | |
| "title": "Knowledge Representation and Sense Disambiguation for Interrogatives in E-HowNet", | |
| "authors": [ | |
| { | |
| "first": "Shu-Ling", | |
| "middle": [], | |
| "last": "Huang", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Academia Sinica", | |
| "location": {} | |
| }, | |
| "email": "slhuang@mail.nhcue.edu.tw" | |
| }, | |
| { | |
| "first": "Keh-Jiann", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Academia Sinica", | |
| "location": {} | |
| }, | |
| "email": "kchen@iis.sinica.edu.tw" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "In order to train machines to 'understand' natural language, we propose a meaning representation mechanism called E-HowNet to encode lexical senses. In this paper, we take interrogatives as examples to demonstrate the mechanisms of semantic representation and composition of interrogative constructions under the framework of E-HowNet. We classify the interrogative words into five classes according to their query types, and represent each type of interrogatives with fine-grained features and operators. The process of semantic composition and the difficulties of representation, such as word sense disambiguation, are addressed. Finally, machine understanding is tested by showing how machines derive the same deep semantic structure for synonymous sentences with different surface structures.", | |
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| "text": "In order to train machines to 'understand' natural language, we propose a meaning representation mechanism called E-HowNet to encode lexical senses. In this paper, we take interrogatives as examples to demonstrate the mechanisms of semantic representation and composition of interrogative constructions under the framework of E-HowNet. We classify the interrogative words into five classes according to their query types, and represent each type of interrogatives with fine-grained features and operators. The process of semantic composition and the difficulties of representation, such as word sense disambiguation, are addressed. Finally, machine understanding is tested by showing how machines derive the same deep semantic structure for synonymous sentences with different surface structures.", | |
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| "section": "Abstract", | |
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| "text": "Electronic dictionaries are designed for the purpose of providing users (or computers) convenient access to relevant knowledge of words to understand language. When we say that a sentence is 'understood', we mean that the concepts and the conceptual relations expressed by the sentence are unambiguously identified and we can make the correct inferences/responses. To have a computer understand a sentence, we must have a framework for representing lexical knowledge and performing semantic composition and disambiguation processes. other concepts. Compared to WordNet, HowNet's architecture provides richer information apart from hyponymy relations. It also enriches relational links between words via encoded feature relations. The advantages of HowNet are (a) its inherent properties are derived from encoded feature relations in addition to hypernym concepts, and (b) information regarding conceptual differences between different concepts and information regarding morph-semantic structure are encoded. Therefore, we adopt a similar mechanism to define word sense in E-HowNet, but represent concepts in a more accurate and flexible way by (a) defining new concepts by well-defined concepts, (b) providing a uniform representational framework for both function words and content words, and (c) embedding semantic composition and decomposition capabilities. More detailed discussions can be seen at [Chen et al. 2005] .", | |
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| "text": "[Chen et al. 2005]", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| "text": "In E-HowNet, we define each lexical sense by the composition of well-defined concepts and/or basic concepts, called sememes in HowNet. The sememes are linked to their sense equivalence WordNet synsets [Fellbaum 1998 ]. Take \u571f\u5730\u516c 'God of earth' as an example:", | |
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| "text": "[Fellbaum 1998", | |
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| "section": "Introduction", | |
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| { | |
| "text": "(1)", | |
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| "text": "\u571f\u5730\u516c Tu di gong 'God of earth' Def:{God|\u795e:telic={manage|\u7ba1\uf9e4:patient={land|\uf9d3\u5730},agent={~}}} Here, 'God' is the hypernym of the target word \u571f\u5730\u516c 'God of earth', 'manage' and 'land' are its related concepts. 'telic', 'patient', and 'agent' are relations which link these concepts. Obviously, to achieve mechanical understanding of natural language, the same or similar concepts must have the same or similar underlying semantic representation. However, natural language can be ambiguous. Different sentences might express the same meaning, and the same sentence can also express different meanings. The following sentences (2) and 3show the former phenomenon, and (4) and (5) show the latter:", | |
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| "text": "(2) \u6211\u80fd\u5426\u62cd\u7167\uff1f Wo neng fou pai zhao? Is it OK for me to take pictures?", | |
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| "text": "(3) \u6211\u53ef\uf967\u53ef\u4ee5\u7167\u76f8\uff1f Wo ke bu ke yi zhao xiang? Can I take photos?", | |
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| "text": "(4) \u571f\u5730\u516c\u6709\u653f\u7b56\u3002Tu di gong you zheng ce. The policy of public sharing of the land.", | |
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| "text": "(5) \u571f\u5730\u516c\u6709\u653f\u7b56\u3002Tu di gong you zheng ce. God of earth has his policy.", | |
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| "text": "Thus, transforming the surface structure of a sentence into a canonical semantic representation", | |
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| "text": "Interrogatives in E-HowNet and simultaneously solving the problem of word sense ambiguity are major research issues. In summary, lexical semantic representation and composition (including disambiguation) are the most demanding techniques for understanding natural language by machines and the design of E-HowNet is aimed for these objectives.", | |
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| "text": "In this paper, we will take interrogatives as examples to demonstrate the mechanism of lexical semantic representation and composition in E-HowNet [Chen et al. 2004] . The goal is to achieve near canonical semantic representation for synonyms and sense equivalent sentences. Take sentences (2) and (3) as examples. Although their syntactic structure and surface strings are very different, by composing lexical sense representations, we hope the machine can 'understand' synonymy of sentences in different surface forms.", | |
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| "text": "Analysis of interrogative constructions is of great interest to linguists, as well as to computer scientists, for example, those who are engaged in QA techniques. Interrogative constructions have played a central role in the development of modern syntactic theory. Ginzburg and A. Sag [2000] have pointed out that the interrogative has been at the heart of work in generative grammar, along with government and binding (GB) theory and head-driven phrase structure grammar (HPSG). Nonetheless, to date, most syntacticians take quite different approaches from semantic and pragmatic points of view on interrogatives. Taking questions in Mandarin Chinese as example, Shao [1996] has summed up the current study of interrogatives and listed the main research themes as follows: the type of question, interrogative particles, querying focus and its answer, degree of doubt, special interrogative sentence patterns, etc. Most of the above themes are purely grammatical analysis. To build a frame-based entity-relation knowledge representation model, we find interrogative construction a good and challenging example because it combines problems of syntax, semantics, and pragmatics.", | |
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| "text": "Ginzburg and A. Sag [2000]", | |
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| "text": "In the following section, we briefly describe the previous works for interrogatives. Then, we introduce our analysis of type classifications for interrogatives and their representation in E-HowNet. Next, we present the semantic composition process of interrogative sentences and the difficulties encountered. We conclude the paper by discussing our results and future work.", | |
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| "text": "Interrogatives in Chinese studies are traditionally attributed to the mood category of syntax. Ma [1935] wrote the first grammar book for Mandarin Chinese. He classified interrogatives into the mood category. Later, Li [1930] and Lv [1942] carried forward his viewpoint and influenced modern linguistic theory on interrogatives deeply. Most linguists consider there to be four grammatical types that explicitly mark an utterance as an interrogative [Lv 1942; Li and Thompson 1997; Tang 1983; Lu 1984; Shao 1996] . First, is a question which can be answered by 'yes' or 'no', called a factual question, true and false interrogative, or yes/no interrogative. Second, is a question which includes Wh-words such as 'who', 'what', and 'when', called a Wh-word interrogative, or an information seeking interrogative. Third, is a question which mentions two or more possible alternative answers, called a disjunctive interrogative or an either/or interrogative. Fourth, is a question which is composed of a statement followed by an A not A form, such as dui bu dui 'right or wrong ' ", | |
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| "text": "[Lv 1942;", | |
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| "end": 480, | |
| "text": "Li and Thompson 1997;", | |
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| "text": "Tang 1983;", | |
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| "text": "Lu 1984;", | |
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| "text": "Shao 1996]", | |
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| "section": "Background", | |
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| "text": "For QA applications, we are more concerned about semantic discrimination of different interrogatives. Therefore, we take a sense-based approach to create a hierarchical classification which is guided by a layered semantic hierarchy of answer types, and eventually classify interrogative sentences into fine-grained classes, shown as (8):", | |
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| "section": "Our Classification of Interrogatives", | |
| "sec_num": "3.1" | |
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| { | |
| "text": "Interrogatives in E-HowNet 8interrogative -(A) true/false (yes/no) interrogative Who knows where I can find this game?", | |
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| "text": "Sentence (9) belongs to the true/false interrogative type and the entire statement is a querying focus. Dissimilarly, Sentence (10) indicates two querying foci through using different interrogatives, 'who' and 'where'. In other words, the true/false interrogative asks truth value of the positive predication of the sentence. Then, the Wh-interrogative is used to ask for information. By analyzing the querying focus, Wh-interrogatives can be further divided into four types: (B-a) asking factual information, such as time, location, quantity, and so forth; (B-b) asking relationship, such as kinship; (B-c) asking opinion or attitude, such as possibility, capacity, volition, etc.; and the last, (B-d) asking to choose an option. Sentence (10) refers to type (B-a). For the remaining types, we give an example of each as follows: Rice rinsing water is acidic or alkaline?", | |
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| "text": "Here, the fine-grain distinctions between interrogative type (B-a) and (B-b) and between interrogative type (A) and (B-c) are clarified below. For instance, sentence (11) refers to type (B-b), but why do we need to separate it from type (B-a) when they both use 'what' to make questions? In sentence (11), the question word \uf9fd\u9ebc she me 'what' asks for relationship but not the type of a frame element or the value of a semantic role as exemplified in (10). 2 The semantic representation of a complex relation is different from the representation of entities. Therefore, we differentiate between interrogative type (B-a) and (B-b). Chen et al. [2004] proposed a compositional mechanism to describe complex relations. For example, we express 'mother in law' as 14:", | |
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| "text": "(14) mother in law def:{human|\u4eba=mother(spouse({x:human|\u4eba}))}", | |
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| "text": "According to the representation model, when our querying focus is a complex relation, we put a question mark before the relation role, such as mother, spouse, parents, etc., to mark the query focus. Representational detail is shown in the next section.", | |
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| "text": "Second, some may argue that there is no distinction between type (A) and (B-c). Example (12) is of type (B-c); we find it may have a yes/no answer, a typical characteristic of type (A).", | |
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| "section": "Our Classification of Interrogatives", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Can he eat hot peppers?", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Ta ke bu ke yi chi la jiao?", | |
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| "text": "However, if we compare the meaning of sentences (12) and (15), we can still find a slight difference between a yes/no question and a question of asking opinion. Sentence (12) has the meaning of asking the hearer's permission, but (15) does not. [1996] has classified the A not A form into five classes according to A's part of speech, shown as follows: (a) A is a copula e.g. \u662f\uf967\u662f shi bu shi 'be or be not' (b) A is a modal word e.g. \u597d\uf967\u597d hao bu hao 'ok or not ok' (c) A is an auxiliary e.g. \u80af\uf967\u80af ken bu ken 'willing or not willing' (d) A is a verb e.g. \u61c2\uf967\u61c2 dong bu dong 'understand or not understand' (e) A is an adjective e.g. \u7f8e\uf967\u7f8e mei bu mei 'beautiful or not beautiful'. From the semantic perspective, we merge (a), (d), (e) and (b), (c) to re-divide these five categories into two categories, i.e. modal A not A interrogatives and other A not A interrogatives.", | |
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| "section": "Ta ke bu ke yi chi la jiao?", | |
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| "text": "In E-HowNet, we made distinctions between content sense and relational sense thereby representing senses of content words and senses of function words in different ways. For instance, in (16), the content words 'bathe' and 'cold water' are represented differently from the function word 'with'. In this example, the function word 'with' plays the role of 'instrument' which links the relation between its argument 'cold water' and the matrix verb 'bathe'. ", | |
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| "section": "Knowledge Representation for Interrogatives", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "{clean|\u4f7f\u6de8: patient={body|\u8eab\u9ad4}, instrument={water|\u6c34:temperature={cold|\uf92e}}}.", | |
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| "section": "Result of semantic composition:", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In much the same way, interrogative words have more relational sense than content sense, so they are defined by semantic role to denote relational sense and use the operator '.Ques.' to mark the querying focus, i.e. the object or its discrimination features which speakers want to know.", | |
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| "section": "Result of semantic composition:", | |
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| "text": "According to the classification of interrogatives above, we represent each type of interrogative as follows: 17 We use two operators, .Ques. and .Option., to denote querying focus or optional items. More detailed sense representations for interrogatives are shown in Table 1 . [Li et al. 1999] . To check the completeness of the above table and to find the distribution of query types, we randomly extracted 1% of the sentences with question marks from the Sinica corpus to see the coverage of the above table, and the results of the distribution are shown in Table 2 . There are 203 sentences, 9 of which do not contain any query word listed in Table 1 . Their query sense is expressed only by a question mark in the end of the sentence. However, we can insert 'ma' \u55ce or 'ne' \u5462 before the question mark to these sentences to make them true/false interrogatives, so statistically they are still counted as true/false interrogatives. We can conclude that most of the questions ask true/false value or factual information. Furthermore, we can classify each of them according to Table 1 . ", | |
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| "text": "Table 2", | |
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| "section": "Result of semantic composition:", | |
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| "text": "In addition to the above interrogatives, there are also some derived interrogative compounds, which are impossible to list comprehensively in our database. Thus, generating their sense representation automatically has to be accomplished. Most interrogative compounds are composed of an interrogative determinative and a noun. Since the number of interrogative determinatives is limited, have already been defined in Table 1 , and listed in (18), we design the rules in (19) to derive the sense representations of interrogative compounds. As the question determinatives \u5e7e ji and \u54ea na are ambiguous, in addition to the general rule (A),(B),(C),(D), we also provide two other rules (E) and (F) to disambiguate their word sense.", | |
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| "text": "Table 1", | |
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| } | |
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| "section": "Knowledge Representation for Interrogative Compounds", | |
| "sec_num": "3.3" | |
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| "text": "The previous discussion has been about semantic representation of lexical senses. To establish a formal system to handle the task of language understanding, we also need to address the issue of semantic composition. To understand Chinese sentences, after the word segmentation and parsing process, we get coarse-grained head-argument event structures of the sentences. Then, we try to project surface syntax onto the semantic structure for establishing truly integrated semantic relations. In Example (20), we identify the sentential head 'lose' after the parsing process, and, based on E-HowNet, the arguments of event 'lose' are 'possessor' and 'possession'; thus we know the 'data' here is the possession of 'lose'. Therefore, the result of composition is as follows: ", | |
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| "section": "Semantic Composition for Interrogatives", | |
| "sec_num": "4." | |
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| "text": "To achieve the goal of automatic semantic composition, we have to solve the problem of word sense disambiguation. In Chinese, \uf9fd\u9ebc she me, \u600e\u9ebc ze me, \u600e\u6a23 ze yang, \u591a duo, \u54ea na, and \u5e7e ji are the most frequently used interrogatives, and they all have ambiguous senses. In addition to Rule (19), their sense disambiguation rules are discussed below:", | |
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| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\uf9fd\u9ebc she me /\u4f55 he/ \u5565 sha \uf9fd\u9ebc she me 'what' plays the grammatical functions of adjective and pronoun, and there are two senses for each function. Accordingly, we generate four rules to disambiguate the word senses of \uf9fd\u9ebc she me, and the details are shown in Table 3 . \u4f55 he and \u5565 sha are its literary and slangy usage, hence, share the same disambiguation rules. In Mandarin Chinese, \uf9ba le, \u9084 hai and the negative words \u6c92 mei, \uf967 bu all mark the endpoint of event; therefore, we use them as constraints to disambiguate the senses of \u600e\u9ebc ze me. Additionally, when a modal word appears between \u600e\u9ebc ze me and the event, normally the event also has an endpoint, that is, \u600e\u9ebc ze me questions 'reason' here. Unlike", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 253, | |
| "end": 260, | |
| "text": "Table 3", | |
| "ref_id": "TABREF10" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Interrogatives in E-HowNet (36) \u660e\u5929\u600e\u6a23 ming tian ze yang 'how about tomorrow' Restore ellipsis:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u660e\u5929\u4e00\u8d77\u53bb\u600e\u6a23 ming tian yi qi qu ze yang 'how about go together tomorrow' def:{go|\u53bb:manner={together|\u5171\u540c},time={tomorrow|\u660e\u5929},willingness={.Ques.}} However, the method for recovering the omitted part of the surface sentence is out of the scope of this paper.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u591a duo \u591a duo 'how' also plays the role of an adverb. It's usually followed by an attribute value, such as \u751c tian 'sweet', \u8070\u660e cong ming 'smart', \u9060 yuan 'far', \u5927 da 'big'. It can be used to express feelings of exclamation or doubt. We can not simply distinguish these two senses by the context, but we need to rely on the tone. For this reason, we will deal only with the senses of doubt. Incidentally, it is always possible to turn a declarative statement into a question by using a slightly rising intonation pattern. For the same reason, we do not deal with such sentences and a few interrogative words, such as \u554a a, \u5427 ba, \u5462 ne, as well.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u591a duo 'how' with interrogative sense can be represent as below: The semantic roles of 'sweetness', 'smartness', 'distance', and ''size' are inferred from their respective values 'sweet|\u751c', 'smart|\u8070\u660e', 'far|\u9060', and 'big|\u5927' by checking the taxonomy of feature-value hierarchy in E-HowNet.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "(37) \u591a duo", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sense Disambiguation", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "For the disambiguation of \u54ea and \u5e7e, please see Section 3.3 (19) rules (E) and (F).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "\u54ea na \u5e7e ji", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "To achieve near canonical semantic representation, we studied the semantic representation and composition of interrogatives. According to the semantic classification of interrogatives, we represent interrogatives in a hierarchy as follows: Although the surface structures of (2), (3) are different, we find that their result of composition is the same. It means that, by means of E-HowNet representation, a machine can judge the sense similarity of words, phrases, and sentences and achieve machine understanding. However, this is only an illustration by example. For future research, we will implement a parsing system incorporated with the E-HowNet model to perform semantic composition process practically. To achieve this goal, apart from sense disambiguation, we find that discordance between syntactic structure and semantic relations is another critical problem. Take The E-HowNet sense representation of (39) is: def:{hard|\u8f9b\u82e6:theme={travel|\uf983\ufa08:distance={far|\u9060}},degree={very|\u5f88},truth={.Ques.}}", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 870, | |
| "end": 874, | |
| "text": "Take", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Works", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "Comparing the semantic representation with syntactic structure, we find rhetorical interrogative \uf967\u662f\u55ce bu shi ma 'Isn't it' is segmented into three words in syntax analysis, but in the semantic point of view, they are integrated into one word and represented as 'truth={.Ques.}'. There are still many types of discordance between syntactic structure and semantic relations that need to be studied. Furthermore, we have to find the mapping rules and match coarse-grained syntactic arguments to fine-grained semantic relations in the future. These results are applicable to both declarative sentences and interrogative sentences.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Works", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "In conclusion, this study sheds new light on designing better and accurate question-answering systems because E-HowNet representation of questions not only represents their senses, but also marks the focused information to be answered. In addition, the proposed representational scheme also provides a way to convert a given sentence into a near-canonical sense representation. Therefore, the design of Chinese QA system will be our future task.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Works", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "Tag interrogative is formed by adding a short A not A question form of certain verbs as a tag to a statement. In this paper, we regard it as a general A not A question type due to the same semantic performance.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The disambiguation of \uf9fd\u9ebc, see section 4.1.1.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "In this paper, our focus is semantic representation, so we don't discuss the interrogative words '\u554a a';'\u5440 a' ; '\u56c9 luo'; '\u4e4e hu'; '\u5427 ba' or '\u5594 wo'. This is because the tone decides if they are interrogative words or not.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The meaningful or dummy measure words are listed in Tai C.H. et al. \"A Semantic Composition Method for Deriving Sense Representations of Determinative-Measure Compounds in E-HowNet\" in the proceeding of ROCLING 2008, Taipei, Taiwan.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "This research was supported in part by the National Science Council under a Center Excellence Grant NSC 96-2752-E-001-001-PAE and Grant N S C96-2221-E-001-009.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgement", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "this, when \u600e\u9ebc ze me appears between the modal word and event, the event does not have an endpoint, so \u600e\u9ebc ze me questions 'means' instead. Their differences can be found in example (33). In the case of entity+\u600e\u6a23 ze yang /\u5982\u4f55 ru he, it usually happens when the main verb of the sentence is omitted. In such cases, we must first recover the omitted part of the surface sentence based on the context then infer the complete sense. For example,", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "annex", | |
| "sec_num": null | |
| } | |
| ], | |
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| { | |
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| "raw_text": "\u99ac\u5efa\u5fe0(Ma, Jianzhong)\uff0c\u99ac\u6c0f\u6587\u901a\uff0c\u5546\u52d9\u5370\u66f8\u9928\uff0c\u53f0\uf963\uff0c1935. Interrogatives in E-HowNet", | |
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| "raw_text": "\uf9d3\u5109\u660e(Lu, Jianming)\uff0c \"\u95dc\u65bc\u73fe\u4ee3\u6f22\u8a9e\uf9e8\u7684\u7591\u554f\u8a9e\u6c23\u8a5e\"\uff0c\u6536\uf93f\u65bc\u4e2d\u570b\u8a9e\u6587\uff0c\u7b2c\u4e94\u671f\uff0c1984, pp. 330-332.", | |
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| "title": "Chinhsi)\uff0c\u65b0\u8457\u570b\u8a9e\u6587\u6cd5(Chinese grammar on the national language)\uff0c\u5546\u52d9\u5370\u66f8 \u9928\uff0c\u53f0\uf963\uff0c1930", | |
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| { | |
| "first": "", | |
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| "year": null, | |
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| "raw_text": "\uf989\u9326\u7199(Li, Chinhsi)\uff0c\u65b0\u8457\u570b\u8a9e\u6587\u6cd5(Chinese grammar on the national language)\uff0c\u5546\u52d9\u5370\u66f8 \u9928\uff0c\u53f0\uf963\uff0c1930.", | |
| "links": null | |
| } | |
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| "text": "Does he like to eat hot peppers? Therefore, in Mandarin Chinese,\u53ef\uf967\u53ef\u4ee5 ke bu ke yi 'can or cannot' and \u559c\uf967\u559c\u6b61 xi bu xi huan 'like or do not like' both have the A not A form, but from the semantic point of view, they belong to different types. Shao", | |
| "num": null, | |
| "content": "<table><tr><td>Interrogatives in E-HowNet</td></tr><tr><td>(15) \u4ed6\u559c\uf967\u559c\u6b61\u5403\u8fa3\u6912\uff1f</td></tr><tr><td>Ta xi bu xi huan chi la jiao?</td></tr></table>", | |
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| "TABREF5": { | |
| "text": "The interrogatives above are gathered from Li and Thompson's analysis and are integrated by checking over 1000 question titles manually in Baidu knows", | |
| "num": null, | |
| "content": "<table><tr><td>Question types</td><td>Words</td><td>Sense representation</td></tr><tr><td/><td>\u55ce ma 3</td><td/></tr><tr><td>true/false interrogatives</td><td/><td>def: truth={.Ques.}</td></tr><tr><td/><td>A not A</td><td/></tr><tr><td/><td>\u8ab0 shei 'who'</td><td>def: participant={animate: formal={.Ques.}}</td></tr><tr><td/><td>\u5e7e\u9ede\u9418 ji dian zhong ' what time'</td><td>def: time={.Ques.}</td></tr><tr><td/><td>\uf9fd\u9ebc 1 she me 'what'</td><td/></tr><tr><td/><td>\u4f55 1 he 'what'</td><td>def: participant={inanimate:formal={.Ques.}}</td></tr><tr><td>Wh-word interrogatives:</td><td>\u5565 1 sha 'what'</td><td/></tr><tr><td>asking factual information</td><td>\uf9fd\u9ebc 2 she me 'what'</td><td/></tr><tr><td/><td>\u4f55 2 he 'what'</td><td>def: formal={.Ques.}</td></tr><tr><td/><td>\u5565 2 sha 'what'</td><td/></tr><tr><td/><td>\u70ba\u4f55 wei he 'why'</td><td/></tr><tr><td/><td>\u4f55\u4ee5 he yi 'why'</td><td>def: reason={.Ques.}</td></tr><tr><td/><td>\u4f55\uf967 he bu 'why not'</td><td/></tr></table>", | |
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| "content": "<table><tr><td>Question types</td><td>Words</td><td colspan=\"2\">Number Total(203)</td></tr><tr><td/><td>\u55ce ma</td><td>50</td><td/></tr><tr><td/><td>\u5462 ni</td><td>16</td><td/></tr><tr><td/><td>\u662f\u5426 shi fou 'whether'</td><td>10</td><td/></tr><tr><td>true/false interrogatives</td><td>\u6709\u6c92\u6709 you mei you 'have'</td><td>1</td><td>98</td></tr><tr><td/><td>\u662f\uf967\u662f shi bu shi 'is it'</td><td>6</td><td/></tr><tr><td/><td>\uf967\u662f\u55ce bu shi ma 'isn't it'</td><td>1</td><td/></tr><tr><td/><td>A not A</td><td>14</td><td/></tr><tr><td/><td>\u8ab0 shei 'who'</td><td>4</td><td/></tr><tr><td/><td>\u5e7e\u9ede\u9418 ji dian zhong 'what time'</td><td>2</td><td/></tr><tr><td/><td>\uf9fd\u9ebc 1 she me 'what'</td><td>14</td><td/></tr><tr><td/><td>\u4f55 1 he 'what'</td><td>1</td><td/></tr><tr><td/><td>\u5565 1 sha 'what'</td><td>0</td><td/></tr><tr><td>Wh-word interrogatives: asking factual information</td><td>\uf9fd\u9ebc 2 she me 'what'</td><td>13</td><td>94</td></tr><tr><td/><td>\u4f55 2 he 'what'</td><td>2</td><td/></tr><tr><td/><td>\u5565 2 sha 'what'</td><td>0</td><td/></tr><tr><td/><td>\u70ba\u4f55 wei he 'why'</td><td>2</td><td/></tr><tr><td/><td>\u4f55\u4ee5 he yi 'why'</td><td>0</td><td/></tr><tr><td/><td>\u4f55\uf967 he bu 'why not'</td><td>1</td><td/></tr></table>", | |
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| "text": "The rules for deriving sense representations of interrogative compounds If the morphological structure of the interrogative compound is ID+head. \u4f55\u6642 he shi 'when' =def: time={.Ques.} e.g. \uf9fd\u9ebc\u5f62\uf9fa she me xing zhuang 'what shape' =def: shape={.Ques.} e.g. \u54ea\u8272 na se 'what color' =def: color={.Ques.}}", | |
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| "content": "<table><tr><td colspan=\"3\">Interrogatives in E-HowNet</td></tr><tr><td colspan=\"3\">(D) if head=MM(meaningful measure words)</td><td>then</td><td>def: {MM: {ID} }</td></tr><tr><td colspan=\"4\">e.g. \u591a\u5c11\u7897 duo shao wan 'how many bowls' =def:container={bowl|\u7897:</td></tr><tr><td>quantity={.Ques.}}</td><td/><td/></tr><tr><td>Other rules,</td><td/><td/></tr><tr><td colspan=\"4\">(E) while {\u7b2c di,\uf9b6\u62dc li bai,\u661f\u671f xing qi,\u897f\u5143 xi yuan,\u6c11\u570b ming quo}+\u5e7e ji</td></tr><tr><td colspan=\"4\">or \u5e7e ji + {\u9ede(\u9418)dain(zhong),\u6708 yue,\u865f hao,\u6b72 sui,\u9031\uf98e zhou nian,\uf98e nian}</td></tr><tr><td colspan=\"4\">(18) The sense representation of interrogative determinatives (ID)</td></tr><tr><td colspan=\"2\">\uf9fd\u9ebc she me /\u4f55 he / \u5565 sha 'what'</td><td colspan=\"2\">def:formal={.Ques.}</td></tr><tr><td>\u591a\u5c11 duo shao 'how many'</td><td/><td colspan=\"2\">def:quantity={.Ques.}</td></tr><tr><td>\u5e7e ji 'how many'or 'what'</td><td/><td colspan=\"2\">def:quantity={.Ques.}or</td></tr><tr><td>def:ordinal={.Ques.}</td><td/><td/></tr><tr><td>\u54ea na 'which'or 'where'</td><td/><td colspan=\"2\">def:quantifier={.Ques.}or</td></tr><tr><td>def:location={.Ques.}</td><td/><td/></tr><tr><td>(19) (A) if head !=semantic role</td><td/><td>then</td><td>def: {head:ID}</td></tr><tr><td/><td/><td/><td>}}.</td></tr><tr><td>Similarily,</td><td/><td/></tr><tr><td colspan=\"4\">e.g. \u5e7e\u4eba ji ren 'how many people' =def: {human|\u4eba:quantity={.Ques.}}</td></tr><tr><td>(B) if head=semantic role</td><td>then</td><td colspan=\"2\">def: role={.Ques.}</td></tr><tr><td colspan=\"3\">e.g. (C) if head=DM(dummy measure words) then</td><td>def: ID</td></tr></table>", | |
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| "content": "<table><tr><td>Rules of disambiguation</td><td colspan=\"2\">Examples & E-HowNet representation</td></tr><tr><td>adjective 1:</td><td/><td/></tr><tr><td>Case 1: if \uf9fd\u9ebc/\u4f55/\u5565+semantic role</td><td colspan=\"2\">\uf9fd\u9ebc\u6642\u9593 she me shi jian 'what time' def: time={.Ques.}</td></tr><tr><td>then \uf9fd\u9ebc/\u4f55/\u5565=def: role={.Ques.}</td><td>\u5565\u50f9\u9322 sha jia qian 'what price'</td><td>def:cost={.Ques.}</td></tr><tr><td>(\uf9fd\u9ebc ask the value of the semantic role.)</td><td>\u4f55\u5730 he di 'what place'</td><td>def:location={.Ques.}</td></tr><tr><td>adjective 2:</td><td>\u4f55\u4eba he ren 'what person'</td><td/></tr><tr><td>Case 2: if \uf9fd\u9ebc/\u4f55/\u5565+entity (nominalized</td><td>def: {human|\u4eba: formal={.Ques.}}</td><td/></tr><tr><td>verbs are included ) then \uf9fd\u9ebc/\u4f55/\u5565=def: {entity:formal={.Ques.}} (\uf9fd\u9ebc ask the type/restriction of a frame element/ participant role.)</td><td colspan=\"2\">\u5565\u8b8a\u5316 sha bian hua 'what change' def: {change|\u8b8a\u5316:formal={.Ques.}} \uf9fd\u9ebc\uf967\u540c she me bu tong 'what difference'</td></tr><tr><td/><td colspan=\"2\">def: {difference|\uf967\u540c:formal={.Ques.}}</td></tr><tr><td>pronoun 1: verb+\uf9fd\u9ebc/\u4f55/\u5565</td><td>\u5403\uf9fd\u9ebc chi she me 'eat what'</td><td/></tr><tr><td>Case 3: if \uf9fd\u9ebc use as an interrogative pronoun then \uf9fd\u9ebc/\u4f55/\u5565 =def:{event:participant={.Ques.}}</td><td>def: {eat|\u5403: patient={.Ques.}} \uf96f\u5565 shuo sha 'talk what'</td><td/></tr><tr><td/><td>def: {speak|\uf96f:content={.Ques.}}</td><td/></tr><tr><td>pronoun 2: verb+\uf9fd\u9ebc/\u4f55/\u5565 or \uf9fd\u9ebc/\u5565+verb</td><td colspan=\"2\">\u62ff\uf9fd\u9ebc\u90fd\u53ef na she me dou ke 'It's OK to get anything'</td></tr><tr><td>Case 4: if \uf9fd\u9ebc use as an indefinite pronoun</td><td colspan=\"2\">def: {hold|\u62ff:patient={entity: quantity={all}}}</td></tr><tr><td>Then \uf9fd\u9ebc/\u4f55/\u5565={event:participant={x}} or{event:participant={entity: quantity={all}}}</td><td colspan=\"2\">\uf9fd\u9ebc\u4e5f\uf967\u6015 she me ye bu pa 'Be afraid of nothing' def: {.not.fear|\u5bb3\u6015:cause={entity: quantity={all}}}</td></tr></table>", | |
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